Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add rules/doitmagic/rag-code-mcp/cursorrulesgit clone --depth 1 https://github.com/doITmagic/rag-code-mcpWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00175 | $0.00175 |
| Opus 5 | $0.00088 | $0.00088 |
| Sonnet 5 | $0.00035 | $0.00035 |
| Haiku 4.5 | $0.00017 | $0.00017 |
Grade A, and why
cursorrules scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
Cursor AI Rules - RagCode MCP
⚖️ The Golden Rule
For any information about the codebase (structure, logic, or usage), you MUST use RagCode MCP tools.
Never guess code details from memory; always search the local index first using search_code or get_function_details.
Guidelines
- Context First: Always call
search_codewhen starting a task to see where relevant logic exists. - Actual Code: Use
get_function_detailsto read the implementation of a function instead of assuming what it does. - Workspace Detection: Always provide the current
file_pathto the tools so they can identify the correct project/workspace. - No Guesswork: If you don't find something, index the workspace using
index_workspaceand search again.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- yesterday First seen · 12 lines · 175 tokens per session scan A c91d62b77792
cursorrules is a cursor rule published in the GitHub repository doITmagic/rag-code-mcp (52 stars, last pushed 14d ago), licensed MIT. It adds 175 tokens to every session, about $0.0009 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other cursor rules, from other repositories
cursorrules
AGENTS.md.
miro-best-practices
MCP server for controlling Miro whiteboards with AI assistants.
plan-execution-loop
Execute a docs/plans/.md slice with subagent implement → evidence-based audit → fix until 90+ — invoke manually when running a plan.
plan-feature
Framework for planning a new feature end-to-end — use when asked to plan or design a new module.
refactor-large-files
Guidance for splitting large route files into maintainable pieces.
solidjs-data-fetching
Route data fetching — onMount, signals, alive guard, batch; never createResource.